保持分布的下采样numpy数组

import numpy as np

def downsample_array(array, labels, ratio):
    unique_labels = np.unique(labels)  # 获取唯一的标签值
    sampled_array = np.empty((0, array.shape[1]))  # 初始化采样后的数组
    sampled_labels = np.empty(0, dtype=labels.dtype)  # 初始化采样后的标签

    for label in unique_labels:
        label_array = array[labels == label]  # 根据标签值获取对应的子数组
        label_sampled_array = label_array[:int(len(label_array) * ratio)]  # 根据下采样比例截取子数组
        sampled_array = np.concatenate((sampled_array, label_sampled_array))  # 将截取的子数组添加到采样后的数组中
        sampled_labels = np.concatenate((sampled_labels, np.full(len(label_sampled_array), label, dtype=labels.dtype)))  # 添加对应的标签

    return sampled_array, sampled_labels

# 示例用法
array = np.random.rand(1000, 10)  # 原始数组
labels = np.random.randint(0, 3, size=1000)  # 对应的标签，取值为0、1、2
ratio = 0.5  # 下采样比例

sampled_array, sampled_labels = downsample_array(array, labels, ratio)